A method, device, equipment and storage medium for denoising laser point cloud in underground roadway

Abstract

The present invention relates to the technical field of laser point cloud data processing, and discloses a method, device, equipment and storage medium for denoising laser point cloud data in underground roadways. The method includes: obtaining laser point cloud data and positioning trajectory data of a roadway by using mobile three-dimensional laser scanning technology; dividing the laser point cloud data along the roadway direction according to the positioning trajectory data to form multiple sections of profile laser point cloud data; dividing each section of profile laser point cloud data on a two-dimensional plane according to the positioning trajectory data to form multiple local target point cloud ranges; traversing all laser points within each local target point cloud range, calculating the Euclidean distance between each laser point and the positioning trajectory data, and removing noise points according to the Euclidean distance. This application can efficiently process a large amount of laser point cloud data, greatly reduce the in-office data processing time, improve the accuracy and reliability of the data, and provide strong support for subsequent applications such as roadway structure recognition, three-dimensional modeling, and volume calculation.

Description

Technical Field

[0001] The present invention relates to the technical field of laser point cloud data processing, and particularly to a method, device, equipment and storage medium for denoising laser point cloud in underground roadways. Background Art

[0002] Based on the mobile three-dimensional laser scanning technology, the laser point cloud data of underground roadways can be obtained quickly and continuously. However, the obtained laser point cloud data of roadways often contains many noise points, such as trailing points generated by dynamic pedestrians or vehicles, and outlier points generated by water vapor or dust. These noise point clouds seriously affect the later application effects of point clouds, such as three-dimensional modeling, overbreak and underbreak analysis, and volume calculation.

[0003] Currently, there are two mainstream denoising methods. The first method is through point cloud filtering, and the second method is through manual interaction. The disadvantage of denoising through point cloud filtering is that it is necessary to traverse each laser point, with slow running speed and high dependence on parameters for denoising effect, and often deletes valid feature points that users are interested in. The disadvantage of denoising through manual interaction is that it is time-consuming and laborious, and the automation level is low. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to overcome the deficiencies in the prior art and provide a method, device, equipment and storage medium for denoising laser point cloud in underground roadways.

[0005] The present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present disclosure provides a method for denoising laser point cloud in underground roadways, the method including:

[0007] Utilize the mobile three-dimensional laser scanning technology to obtain the laser point cloud data and positioning trajectory data of the roadway;

[0008] According to the positioning trajectory data, cut the laser point cloud data along the roadway direction to form multiple segments of profile laser point cloud data;

[0009] According to the positioning trajectory data, cut each segment of the profile laser point cloud data on a two-dimensional plane to form multiple local target point cloud ranges;

[0010] Traverse all laser points within each local target point cloud range, calculate the Euclidean distance between each laser point and the positioning trajectory data, and eliminate noise points according to the Euclidean distance.

[0011] Optionally, the laser point cloud data is discrete laser points, the positioning trajectory data is sequenced positioning trajectory points, and the positioning trajectory points are distributed along the roadway direction.

[0012] Optionally, splitting the laser point cloud data along the roadway direction according to the positioning trajectory data to form multiple segments of profile laser point cloud data, including:

[0013] Starting from one side of the starting or ending point of the positioning trajectory points, sequentially intercepting all the laser point cloud data within a preset number of the positioning trajectory points along the roadway direction to form multiple segments of profile laser point cloud data.

[0014] Optionally, splitting each segment of the profile laser point cloud data in a two-dimensional plane according to the positioning trajectory data to form multiple local target point cloud ranges, including:

[0015] In each segment of the profile laser point cloud data, projecting each segment of the profile laser point cloud data according to the positioning trajectory data in the two-dimensional plane;

[0016] Using the positioning trajectory data as endpoints, equally anglingly splitting the projected profile laser point cloud data segments in the two-dimensional plane to form multiple local target point cloud ranges.

[0017] Optionally, in each segment of the profile laser point cloud data, projecting each segment of the profile laser point cloud data according to the positioning trajectory data in the two-dimensional plane, including:

[0018] Determining a normal vector according to the positioning trajectory data, and constructing a projection plane based on the positioning trajectory data and the normal vector;

[0019] Projecting each segment of the profile laser point cloud data onto the projection plane, and determining the point cloud coordinates of each laser point according to the distance from each laser point in each segment of the profile laser point cloud data to the projection plane.

[0020] Optionally, using the positioning trajectory data as endpoints, equally anglingly splitting the projected profile laser point cloud data segments in the two-dimensional plane to form multiple local target point cloud ranges, including:

[0021] Using the positioning trajectory data as endpoints, dividing each projected segment of the profile laser point cloud data into a fan-shaped segment at every preset angle, and splitting it into multiple local target point cloud ranges;

[0022] Calculating the polar coordinates of each laser point in each local target point cloud range.

[0023] Optionally, traversing all the laser points in each local target point cloud range, calculating the Euclidean distance between each laser point and the positioning trajectory data, and removing noise points according to the Euclidean distance, including:

[0024] Calculating the Euclidean distance between each laser point within the local target point cloud and the positioning trajectory data, and calculating the average and standard deviation of the Euclidean distances of all laser points;

[0025] The absolute value of the difference between the Euclidean distance of each laser point and the average value is calculated, and the laser points whose absolute value is greater than a preset multiple of the standard deviation are determined as the noise points, and the noise points are removed.

[0026] In a second aspect, an embodiment of the present disclosure provides a device for denoising a laser point cloud of an underground tunnel, the device comprising:

[0027] An acquisition module, used to acquire laser point cloud data and positioning trajectory data of the lane by using mobile 3D laser scanning technology;

[0028] The first segmentation module is used to segment the laser point cloud data along the direction of the lane according to the positioning trajectory data to form multiple sections of profile laser point cloud data;

[0029] A second segmentation module is used to segment each section of the profile laser point cloud data on a two-dimensional plane according to the positioning trajectory data to form a plurality of local target point cloud ranges;

[0030] The elimination module is used to traverse all laser points within the range of each local target point cloud, calculate the Euclidean distance between each laser point and the positioning trajectory data, and eliminate noise points according to the Euclidean distance.

[0031] In a third aspect, a computer device is provided in an embodiment of the present disclosure, wherein the computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the underground tunnel laser point cloud denoising method described in the first aspect are implemented.

[0032] In a fourth aspect, a computer-readable storage medium is provided in an embodiment of the present disclosure, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the underground tunnel laser point cloud denoising method described in the first aspect are implemented.

[0033] Beneficial effects of this application:

[0034] The downhole roadway laser point cloud denoising method provided by the embodiment of the present application includes: acquiring laser point cloud data and positioning trajectory data of the roadway by using mobile three-dimensional laser scanning technology; splitting the laser point cloud data along the roadway direction according to the positioning trajectory data to form multiple sections of profile laser point cloud data; splitting each section of the profile laser point cloud data on a two-dimensional plane according to the positioning trajectory data to form multiple local target point cloud ranges; traversing all laser points within each local target point cloud range, calculating the Euclidean distance between each laser point and the positioning trajectory data, and removing noise points according to the Euclidean distance. The present application can efficiently process a large amount of laser point cloud data, greatly reducing the in-office data processing time, improving the accuracy and reliability of the data, and providing strong support for subsequent applications such as roadway structure recognition, three-dimensional modeling, and volume calculation.

[0035] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, makes the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts. In each drawing, similar components are numbered similarly.

[0037] Figure 1 FIG. 1 shows one of the flowcharts of a downhole roadway laser point cloud denoising method provided by the embodiment of the present application;

[0038] Figure 2 FIG. 2 shows a schematic diagram of multiple sections of profile laser point cloud data provided by the embodiment of the present application;

[0039] Figure 3 FIG. 3 shows another flowchart of a downhole roadway laser point cloud denoising method provided by the embodiment of the present application;

[0040] Figure 4 FIG. 4 shows yet another flowchart of a downhole roadway laser point cloud denoising method provided by the embodiment of the present application;

[0041] Figure 5 FIG. 5 shows a schematic diagram of splitting profile laser point cloud data on a two-dimensional plane according to positioning trajectory data provided by the embodiment of the present application;

[0042] Figure 6 FIG. 6 shows a schematic diagram of a local target point cloud range provided by the embodiment of the present application;

[0043] Figure 7 The figure shows a schematic structural diagram of an underground roadway laser point cloud denoising device provided by an embodiment of the present application;

[0044] Figure 8 The figure shows a schematic structural diagram of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0045] The embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals indicate the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation of the present invention.

[0046] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there can also be a middle element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be a middle element at the same time. On the contrary, when an element is referred to as being "directly on" another element, there is no middle element. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for the purpose of illustration.

[0047] In the present invention, unless otherwise clearly defined and limited, the terms "install", "connect", "connect", "fix" and other terms should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0048] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality" means two or more unless otherwise specifically defined.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs. The terms used in the description of the template herein are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0050] Embodiment 1

[0051] As Figure 1 shown, it is a flowchart of a method for denoising laser point cloud in an underground roadway in an embodiment of the present application. The method for denoising laser point cloud in an underground roadway provided by the embodiment of the present application includes the following steps:

[0052] Step S110, obtaining laser point cloud data and positioning trajectory data of the roadway by using mobile three-dimensional laser scanning technology.

[0053] In this embodiment, mobile three-dimensional laser scanning technology can be used to obtain laser point cloud data and positioning trajectory data of a continuous roadway. Among them, the laser point cloud data is composed of a series of discrete laser points (x, y, z), representing the three-dimensional coordinates of the roadway surface; the positioning trajectory data is composed of timed positioning trajectory points (T, X, Y, Z), where T represents the timestamp, and X, Y, Z represent the three-dimensional coordinates of the positioning point. The positioning trajectory points are usually distributed along the roadway direction and have a relatively high output frequency (such as above 10Hz).

[0054] It should be noted that in the current prior art, to obtain laser point cloud data of a continuous area, it is necessary to first use a fixed three-dimensional laser scanner to scan and obtain laser point cloud data of a small area, and then splice multiple point cloud data through in-office data processing, which is time-consuming and laborious. In this application, through a mobile three-dimensional laser scanner, continuous laser point cloud data of an area can be obtained by virtue of real-time laser SLAM (Simultaneous Localization and Mapping) technology: the mobile carrier is equipped with sensors such as lidar and inertial navigation. The lidar can obtain the geometric information of the surrounding environment in real time. The rotation amount and translation amount will be output during the splicing (or registration) process of two frames of laser point cloud data. The rotation amount constitutes the attitude data, and the translation amount constitutes the position data. Therefore, the position information and attitude information will be formed during the splicing process of two frames of laser point cloud data, that is, the "self-positioning information" (i.e., positioning trajectory points) of the mobile carrier. Then, according to the "self-positioning information", the final splicing of these two frames of laser point cloud is completed. This process is called mapping. Repeating this process, the positioning-mapping-positioning-mapping process is finally completed, and the continuous splicing of multiple frames of laser point cloud data is completed to form the overall laser point cloud data. The positioning and mapping processes complement each other, promote each other, and are carried out synchronously, greatly improving the surveying and mapping efficiency.

[0055] Through the mobile three-dimensional laser scanning technology, the above method can obtain the three-dimensional coordinate information of the roadway surface and the sequential three-dimensional coordinate information of the positioning points in real time, avoiding the cumbersome process of stitching multiple small-scale point cloud data in the traditional method, thereby reducing the errors and omissions that may occur in the data stitching process, improving the integrity and accuracy of the data, and providing necessary and accurate original data for subsequent processing.

[0056] Step S120: Cut the laser point cloud data along the roadway direction according to the positioning trajectory data to form multiple segments of profile laser point cloud data.

[0057] Understandably, the positioning trajectory points with time series are expressed as follows:

[0058]

[0059] As Figure 2 shown, the black dots are the positioning trajectory points. Starting from one side of the start or end of the positioning trajectory points, all the laser point cloud data with a segment length of k within the range of the positioning trajectory points are sequentially intercepted along the roadway direction to form multiple segments of profile laser point cloud data.

[0060] It should be noted that the value of k can be adjusted according to the roadway length, data volume, and processing requirements. For example, the segment length k is dynamically selected according to the roadway curvature (calculated by the second derivative of the positioning trajectory points). When the curvature is large, k decreases; when the curvature is small, k increases, avoiding over-segmentation of straight segments or under-segmentation of curved segments. This embodiment does not make a limitation on this.

[0061] The above method cuts the entire roadway laser point cloud data into multiple segments of profile data, reducing the complexity and calculation amount of data processing. The segmented profile data is easier to analyze and process, which helps to more accurately identify the roadway structure in the subsequent steps.

[0062] Step S130: Cut each segment of the profile laser point cloud data on a two-dimensional plane according to the positioning trajectory data to form multiple local target point cloud ranges.

[0063] Specifically, first, in each segment of the profile laser point cloud data, project each segment of the profile laser point cloud data onto a two-dimensional plane according to the positioning trajectory data. As Figure 3 shown, the specific steps include:

[0064] Step S131: Determine the normal vector according to the positioning trajectory data, and construct a projection plane according to the positioning trajectory data and the normal vector;

[0065] Step S132: Project the profile laser point cloud data of each segment onto the projection plane, and determine the point cloud coordinates of each laser point according to the distance from each laser point in the profile laser point cloud data of each segment to the projection plane.

[0066] Understandably, in the profile laser point cloud data of each segment, using the positioning trajectory data as an endpoint, and using the connection vector between it and the next positioning trajectory data as the normal vector to construct the projection plane, the equation expression of the projection plane is:

[0067]

[0068] In the formula, , , , .

[0069] Next, project the profile laser point cloud data of each segment onto the projection plane, and determine the point cloud coordinates of each laser point according to the distance from each laser point in the profile laser point cloud data of each segment to the projection plane :

[0070]

[0071]

[0072]

[0073] In the formula, is the distance from the i-th laser point to the projection plane.

[0074] The above steps construct the projection plane according to the positioning trajectory data and the normal vector, ensuring the accuracy and effectiveness of the projection. By converting the three-dimensional point cloud data into two-dimensional plane data through the projection plane equation, the data processing process is simplified while key information is retained.

[0075] Furthermore, as Figure 4 shown, using the positioning trajectory data as endpoints, equally angularly divide the projected profile laser point cloud data of each segment on the two-dimensional plane to form multiple local target point cloud ranges. The specific steps include:

[0076] Step S133: Using the positioning trajectory data as endpoints, divide each projected segment of the profile laser point cloud data into a fan-shaped segment every preset angle, and cut it into multiple local target point cloud ranges;

[0077] Step S134: Calculate the polar coordinates of each laser point in each local target point cloud range.

[0078] Understandably, as Figure 5 shown, using the positioning trajectory data as endpoints, each section of the projected profile laser point cloud data is divided into a fan-shaped segment at preset angles and is divided into a total of local target point cloud ranges, and the polar coordinates of all the projected laser points are calculated for subsequent denoising processing based on the Euclidean distance.

[0079] It should be noted that in this embodiment, the preset angle of segmentation can be dynamically adjusted according to the actual width and shape of the roadway . In a wider roadway, the segmentation angle can be increased to cover more areas; in a narrow roadway, the segmentation angle is reduced to improve the processing accuracy. The specific values can be determined according to the actual situation, and the embodiments of the present application do not limit this.

[0080] The above steps further refine the profile laser point cloud data into multiple local target point cloud ranges, which helps to more accurately identify and process the detailed features in the roadway. The equal-angle segmentation method ensures that each local target point cloud range has a similar size and shape, facilitating unified processing and analysis in subsequent steps.

[0081] Step S140: Traverse all the laser points within each of the local target point cloud ranges, calculate the Euclidean distance between each laser point and the positioning trajectory data, and remove the noise points according to the Euclidean distance.

[0082] Specifically, within each local target point cloud range, assuming there are m laser points, traverse each laser point and calculate the Euclidean distance between each laser point and the positioning trajectory data . The calculation formula is as follows:

[0083]

[0084] In the formula, is the Euclidean distance between the m-th laser point and the positioning trajectory data. Since the projection is performed on a two-dimensional plane, only the y and z coordinates are considered.

[0085] Next, calculate the average value and the standard deviation of the Euclidean distances of all the laser points. The calculation formulas are as follows:

[0086]

[0087]

[0088] Understandably, as Figure 6As shown in the figure, within each local target point cloud range, the Euclidean distance between the laser points on the roadway wall and the positioning trajectory points should follow a normal distribution, and its geometric relationship is as follows: Noise points belong to outliers. Therefore, within each local target point cloud range, calculate the absolute value of the difference between the Euclidean distance of each laser point and the average value, and determine the laser points with an absolute value greater than a preset multiple n (such as 2 times) of the standard deviation as noise points, that is:

[0089]

[0090] Finally, remove the filtered noise points, such as the trailing shadows generated by dynamic pedestrians or vehicles, and the outliers generated by water vapor and dust. Preferably, after the initial denoising, recheck and denoise the remaining point cloud data to remove the possibly missed noise points, aiming to improve the efficiency of in-office laser point cloud data processing.

[0091] Preferably, in this embodiment, a DQN (Deep Q-Learning) model can be constructed to dynamically optimize the segmentation parameters (k, θ), preset multiple (n), etc. according to the historical denoising effect, form an adaptive denoising closed-loop system, realize intelligent parameter adjustment, reduce manual intervention, and improve the generalization ability of the system to different roadway environments.

[0092] The above method can accurately identify noise points by calculating the Euclidean distance between laser points and positioning trajectory data. Using the characteristics of the normal distribution to statistically analyze the Euclidean distance of laser points can more accurately determine the threshold of noise points. After removing the noise points, the quality and accuracy of the laser point cloud data are improved, providing a reliable data basis for subsequent processing and analysis.

[0093] The downhole roadway laser point cloud denoising method provided by the embodiment of the present application uses the mobile three-dimensional laser scanning technology to obtain the laser point cloud data and positioning trajectory data of the roadway; divides the laser point cloud data along the roadway direction according to the positioning trajectory data to form multiple sections of profile laser point cloud data; divides each section of the profile laser point cloud data on the two-dimensional plane according to the positioning trajectory data to form multiple local target point cloud ranges; traverses all laser points within each local target point cloud range, calculates the Euclidean distance between each laser point and the positioning trajectory data, and removes noise points according to the Euclidean distance. The present application can efficiently process a large amount of laser point cloud data, greatly reduce the in-office data processing time, improve the accuracy and reliability of the data, and provide strong support for subsequent applications such as roadway structure recognition, three-dimensional modeling, and volume calculation.

[0094] Embodiment 2

[0095] As Figure 7As shown in the figure, it is a schematic structural diagram of an underground roadway laser point cloud denoising device 700 in an embodiment of the present application. The device includes:

[0096] An acquisition module 710, configured to acquire laser point cloud data and positioning trajectory data of the roadway by using mobile three-dimensional laser scanning technology;

[0097] A first segmentation module 720, configured to segment the laser point cloud data along the roadway direction according to the positioning trajectory data to form multiple segments of profile laser point cloud data;

[0098] A second segmentation module 730, configured to segment each segment of the profile laser point cloud data in a two-dimensional plane according to the positioning trajectory data to form multiple local target point cloud ranges;

[0099] A rejection module 740, configured to traverse all laser points within each local target point cloud range, calculate the Euclidean distance between each laser point and the positioning trajectory data, and reject noise points according to the Euclidean distance.

[0100] The underground roadway laser point cloud denoising device provided by the embodiment of the present application can implement each process of the underground roadway laser point cloud denoising method corresponding to Embodiment 1, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0101] The underground roadway laser point cloud denoising device provided by the embodiment of the present application can efficiently process a large amount of laser point cloud data, greatly reduce the in-office data processing time, improve the accuracy and reliability of the data, and provide strong support for subsequent applications such as roadway structure recognition, three-dimensional modeling, and volume calculation.

[0102] Embodiment 3

[0103] The embodiment of the present application also provides a computer device. Specifically, please refer to Figure 8 , Figure 8 which is the basic structural block diagram of the computer device in this embodiment.

[0104] The computer device 8 includes a memory 81, a processor 82, and a network interface 83 that are communicatively connected to each other via a system bus. It should be noted that only the computer device 8 having a memory 81, a processor 82, and a network interface 83 is shown in the figure. However, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Among them, those skilled in the art of the present technology can understand that the computer device here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0105] The computer device may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The computer device can interact with the user through a keyboard, a mouse, a remote control, a touchpad, or a voice control device.

[0106] The memory 81 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (such as SD or D-slot compatibility test memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 81 may be an internal storage unit of the computer device 8, such as the hard disk or memory of the computer device 8. In other embodiments, the memory 81 may also be an external storage device of the computer device 8, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 8. Of course, the memory 81 may also include both the internal storage unit and the external storage device of the computer device 8. In this embodiment, the memory 81 is generally used to store the operating system and various application software installed on the computer device 8, such as computer-readable instructions for the slot compatibility test method. In addition, the memory 81 can also be used to temporarily store various data that have been output or will be output.

[0107] In some embodiments, the processor 82 may be a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor, or other downhole roadway laser point cloud denoising chips. The processor 82 is generally used to control the overall operation of the computer device 8. In this embodiment, the processor 82 is used to run the computer-readable instructions stored in the memory 81 or process data, such as running the computer-readable instructions of the slot compatibility test method.

[0108] The network interface 83 may include a wireless network interface or a wired network interface, and this network interface 83 is generally used to establish a communication connection between the computer device 8 and other electronic devices.

[0109] The computer device provided in this embodiment can execute the above-mentioned downhole roadway laser point cloud denoising method. Here, the downhole roadway laser point cloud denoising method can be the downhole roadway laser point cloud denoising methods of the above various embodiments.

[0110] Embodiment 4

[0111] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the downhole roadway laser point cloud denoising method in the embodiment are implemented.

[0112] In this embodiment, the computer-readable storage medium includes flash memory, a hard disk, a multimedia card, a card-type memory (such as an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disc, etc. In some embodiments, the computer-readable storage medium may be an internal storage unit of the computer device, such as the hard disk or memory of the computer device. In other embodiments, the computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk equipped on the computer device, a Smart Media Card (SMC for short), a Secure Digital (SD for short) card, a Flash Card, etc. Of course, the computer-readable storage medium may also include both the internal storage unit and the external storage device of the computer device. In this embodiment, the computer-readable storage medium is generally used to store the operating system and various application software installed on the computer device. In addition, the computer-readable storage medium can also be used to temporarily store various data that have been output or will be output.

[0113] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and structure diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of the present invention. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in alternative implementations, the functions marked in the blocks may occur in a different order from that marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the structure diagram and / or flowchart, as well as the combination of blocks in the structure diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0114] In addition, each functional module or unit in various embodiments of the present invention can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0115] If the above-mentioned functions are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium can be a non-volatile storage medium or a volatile storage medium. For example, the storage medium can be: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, etc., which can store program codes.

[0116] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention.

Claims

1. A method for denoising underground tunnel laser point cloud, characterized in that: The method comprises: Use mobile 3D laser scanning technology to obtain laser point cloud data and positioning trajectory data of the tunnel; According to the positioning trajectory data, the laser point cloud data is divided along the direction of the lane to form multiple sections of profile laser point cloud data; Dividing each section of the profile laser point cloud data on a two-dimensional plane according to the positioning trajectory data to form a plurality of local target point cloud ranges; Traversing all laser points within the range of each local target point cloud, calculating the Euclidean distance between each laser point and the positioning trajectory data, and eliminating noise points according to the Euclidean distance; Wherein, the laser point cloud data are discrete laser points, the positioning trajectory data are time-sequential positioning trajectory points, and the positioning trajectory points are distributed along the direction of the lane; The laser point cloud data is divided along the direction of the lane according to the positioning trajectory data to form multiple sections of profile laser point cloud data, including: Starting from the starting or ending side of the positioning track point, all laser point cloud data within a preset number of the positioning track points are sequentially intercepted along the direction of the lane to form multiple sections of laser point cloud data; Wherein, the laser point cloud data of each section is divided on a two-dimensional plane according to the positioning trajectory data to form a plurality of local target point cloud ranges, including: In each section of the cross-sectional laser point cloud data, projecting each section of the cross-sectional laser point cloud data according to the positioning trajectory data according to a two-dimensional plane; Taking the positioning trajectory data as endpoints, the projected sections of the profile laser point cloud data are divided into equal angles on a two-dimensional plane to form a plurality of local target point cloud ranges; The traversing of all laser points within the range of each local target point cloud, calculating the Euclidean distance between each laser point and the positioning trajectory data, and removing noise points according to the Euclidean distance includes: Calculating the Euclidean distance between each laser point within the local target point cloud and the positioning trajectory data, and calculating the average and standard deviation of the Euclidean distances of all laser points; The absolute value of the difference between the Euclidean distance of each laser point and the average value is calculated, and the laser points whose absolute value is greater than a preset multiple of the standard deviation are determined as the noise points, and the noise points are removed.

2. The underground tunnel laser point cloud denoising method according to claim 1, characterized in that: In each section of the cross-sectional laser point cloud data, projecting each section of the cross-sectional laser point cloud data according to the positioning trajectory data according to a two-dimensional plane includes: Determine a normal vector according to the positioning trajectory data, and construct a projection plane according to the positioning trajectory data and the normal vector; Project each segment of the cross-section laser point cloud data onto the projection plane, and determine the point cloud coordinates of each laser point according to the distance from each laser point in each segment of the cross-section laser point cloud data to the projection plane.

3. The underground tunnel laser point cloud denoising method according to claim 2, characterized in that: The positioning trajectory data is used as an endpoint, and each segment of the projected profile laser point cloud data is divided into equal angles on a two-dimensional plane to form a plurality of local target point cloud ranges, including: Taking the positioning trajectory data as the endpoint, dividing each segment of the projected cross-section laser point cloud data into a fan-shaped segment at every preset angle, and cutting it into a plurality of local target point cloud ranges; The polar coordinates of each laser point in the local target point cloud range are calculated.

4. A laser point cloud denoising device for underground tunnels, characterized in that: The device comprises: An acquisition module, used to acquire laser point cloud data and positioning trajectory data of the lane by using mobile 3D laser scanning technology; The first segmentation module is used to segment the laser point cloud data along the direction of the lane according to the positioning trajectory data to form multiple sections of profile laser point cloud data; A second segmentation module is used to segment each section of the profile laser point cloud data on a two-dimensional plane according to the positioning trajectory data to form a plurality of local target point cloud ranges; A removal module is used to traverse all laser points within the range of each local target point cloud, calculate the Euclidean distance between each laser point and the positioning trajectory data, and remove noise points according to the Euclidean distance; Wherein, the laser point cloud data are discrete laser points, the positioning trajectory data are time-sequential positioning trajectory points, and the positioning trajectory points are distributed along the direction of the lane; The laser point cloud data is divided along the direction of the lane according to the positioning trajectory data to form multiple sections of profile laser point cloud data, including: Starting from the starting or ending side of the positioning track point, all laser point cloud data within a preset number of the positioning track points are sequentially intercepted along the direction of the lane to form multiple sections of laser point cloud data; Wherein, the laser point cloud data of each section is divided on a two-dimensional plane according to the positioning trajectory data to form a plurality of local target point cloud ranges, including: In each section of the cross-sectional laser point cloud data, projecting each section of the cross-sectional laser point cloud data according to the positioning trajectory data according to a two-dimensional plane; Taking the positioning trajectory data as endpoints, the projected sections of the profile laser point cloud data are divided into equal angles on a two-dimensional plane to form a plurality of local target point cloud ranges; The traversing of all laser points within the range of each local target point cloud, calculating the Euclidean distance between each laser point and the positioning trajectory data, and removing noise points according to the Euclidean distance includes: Calculating the Euclidean distance between each laser point within the local target point cloud and the positioning trajectory data, and calculating the average and standard deviation of the Euclidean distances of all laser points; The absolute value of the difference between the Euclidean distance of each laser point and the average value is calculated, and the laser points whose absolute value is greater than a preset multiple of the standard deviation are determined as the noise points, and the noise points are removed.

5. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the underground tunnel laser point cloud denoising method according to any one of claims 1 to 3 when executing the computer program.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the underground tunnel laser point cloud denoising method described in any one of claims 1 to 3 are implemented.

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